Analysis date: 2023-08-08

Depends on

CRC_Xenografts_Batch2_DataProcessing Script

load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")

TODO

Setup

Load libraries and functions

Analysis

DEP

Tyrosine all

Each condition vs ctrl

data_diff_ctrl_vs_E_pY <- test_diff(pY_se_Set3, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set3_form, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## Loading required namespace: reactome.db
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                              pathway      pval      padj
## 1:                         ABC transporter disorders 0.8599034 0.9509233
## 2:            ABC-family proteins mediated transport 0.8599034 0.9509233
## 3:         ADP signalling through P2Y purinoceptor 1 0.5617597 0.9109373
## 4:                             ALK mutants bind TKIs 0.4633663 0.9035512
## 5: APC/C-mediated degradation of cell cycle proteins 0.8599034 0.9509233
## 6:       APC/C:Cdc20 mediated degradation of Securin 0.8599034 0.9509233
##       log2err         ES        NES size leadingEdge
## 1: 0.06143641 -0.4386792 -0.7344914    2  5692,10213
## 2: 0.06143641 -0.4386792 -0.7344914    2  5692,10213
## 3: 0.06479434  0.6136988  0.9532883    2   6714,1432
## 4: 0.08266464  0.7652582  1.0204330    1        1213
## 5: 0.06143641 -0.4386792 -0.7344914    2  5692,10213
## 6: 0.06143641 -0.4386792 -0.7344914    2  5692,10213
data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set3, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set3_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                              pathway      pval      padj
## 1:                         ABC transporter disorders 0.8900256 0.9316588
## 2:            ABC-family proteins mediated transport 0.8900256 0.9316588
## 3:         ADP signalling through P2Y purinoceptor 1 0.6313214 0.9229041
## 4:                             ALK mutants bind TKIs 0.8710317 0.9316588
## 5: APC/C-mediated degradation of cell cycle proteins 0.8900256 0.9316588
## 6:       APC/C:Cdc20 mediated degradation of Securin 0.8900256 0.9316588
##       log2err         ES        NES size leadingEdge
## 1: 0.06252374 -0.4433962 -0.7020923    2  5692,10213
## 2: 0.06252374 -0.4433962 -0.7020923    2  5692,10213
## 3: 0.05748774  0.6179245  0.9222106    2   6714,1432
## 4: 0.05163560  0.5727700  0.7579792    1        1213
## 5: 0.06252374 -0.4433962 -0.7020923    2  5692,10213
## 6: 0.06252374 -0.4433962 -0.7020923    2  5692,10213
## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set3, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set3_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                              pathway      pval      padj
## 1:                         ABC transporter disorders 0.6819338 0.9254138
## 2:            ABC-family proteins mediated transport 0.6819338 0.9254138
## 3:         ADP signalling through P2Y purinoceptor 1 0.2962357 0.9254138
## 4:                             ALK mutants bind TKIs 0.6541667 0.9254138
## 5: APC/C-mediated degradation of cell cycle proteins 0.6819338 0.9254138
## 6:       APC/C:Cdc20 mediated degradation of Securin 0.6819338 0.9254138
##       log2err         ES        NES size leadingEdge
## 1: 0.07550153 -0.5235849 -0.8331696    2  5692,10213
## 2: 0.07550153 -0.5235849 -0.8331696    2  5692,10213
## 3: 0.09721508  0.7500000  1.1463332    2   1432,6714
## 4: 0.06751890  0.6619718  0.8897950    1        1213
## 5: 0.07550153 -0.5235849 -0.8331696    2  5692,10213
## 6: 0.07550153 -0.5235849 -0.8331696    2  5692,10213
## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

EC vs E

data_diff_EC_vs_E_pY <- test_diff(pY_se_Set3, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pY_Set3_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                              pathway      pval      padj
## 1:                         ABC transporter disorders 0.6891089 0.9929295
## 2:            ABC-family proteins mediated transport 0.6891089 0.9929295
## 3:         ADP signalling through P2Y purinoceptor 1 0.4910891 0.9929295
## 4:                             ALK mutants bind TKIs 0.4469697 0.9929295
## 5: APC/C-mediated degradation of cell cycle proteins 0.6891089 0.9929295
## 6:       APC/C:Cdc20 mediated degradation of Securin 0.6891089 0.9929295
##       log2err         ES        NES size leadingEdge
## 1: 0.06252374 -0.5849057 -0.8836174    2  5692,10213
## 2: 0.06252374 -0.5849057 -0.8836174    2  5692,10213
## 3: 0.07955647 -0.6839623 -1.0332622    2   6714,1432
## 4: 0.08220549 -0.7793427 -1.0361237    1        1213
## 5: 0.06252374 -0.5849057 -0.8836174    2  5692,10213
## 6: 0.06252374 -0.5849057 -0.8836174    2  5692,10213
## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set3, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set3_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                              pathway      pval      padj
## 1:                         ABC transporter disorders 0.5988142 0.9476975
## 2:            ABC-family proteins mediated transport 0.5988142 0.9476975
## 3:         ADP signalling through P2Y purinoceptor 1 0.7237903 0.9736555
## 4:                             ALK mutants bind TKIs 0.6119097 0.9476975
## 5: APC/C-mediated degradation of cell cycle proteins 0.5988142 0.9476975
## 6:       APC/C:Cdc20 mediated degradation of Securin 0.5988142 0.9476975
##       log2err         ES        NES size leadingEdge
## 1: 0.06928365 -0.6342152 -0.9579705    2       10213
## 2: 0.06928365 -0.6342152 -0.9579705    2       10213
## 3: 0.06103637  0.5505222  0.8458675    2   1432,6714
## 4: 0.07011322  0.6854460  0.9223450    1        1213
## 5: 0.06928365 -0.6342152 -0.9579705    2       10213
## 6: 0.06928365 -0.6342152 -0.9579705    2       10213
#data_results <- get_df_long(dep)

Serine/Threonine all

Each condition vs ctrl

data_diff_ctrl_vs_E_pST <- test_diff(pST_se_Set3, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
## Warning in fdrtool::fdrtool(res$t, plot = FALSE, verbose = FALSE): There may be
## too few input test statistics for reliable FDR calculations!
## Warning: Censored sample for null model estimation has only size 5 !
dep_ctrl_vs_E_pST <- add_rejections_SH(data_diff_ctrl_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pST, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set3_form, dep_ctrl_vs_E_pST, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                          pathway       pval
## 1:       ADORA2B mediated anti-inflammatory cytokines production 0.05396005
## 2:                                         ALK mutants bind TKIs 0.06464646
## 3:                                      ARMS-mediated activation 0.71179884
## 4:                   AUF1 (hnRNP D0) binds and destabilizes mRNA 0.01461364
## 5:                                      AURKA Activation by TPX2 0.27145086
## 6: Abortive elongation of HIV-1 transcript in the absence of Tat 0.44100580
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.5238163 0.32177592  0.8828829  1.5400349    2    5576,112
## 2: 0.5238163 0.25296112  0.9732143  1.3217310    1        4869
## 3: 0.8764671 0.05986031 -0.6428571 -0.8782148    1         673
## 4: 0.5238163 0.38073040 -0.9910714 -1.3539145    1        3184
## 5: 0.7739840 0.09957912 -0.6846847 -1.1637662    2  22994,1454
## 6: 0.8764671 0.08407456 -0.7589286 -1.0367814    1        7936
## Warning:  we couldn't map to STRING 1% of your identifiers

## Note: Row-scaling applied for this heatmap

data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set3, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
## Warning in fdrtool::fdrtool(res$t, plot = FALSE, verbose = FALSE): There may be
## too few input test statistics for reliable FDR calculations!
dep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set3_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                          pathway       pval
## 1:       ADORA2B mediated anti-inflammatory cytokines production 0.10215054
## 2:                                         ALK mutants bind TKIs 0.09623431
## 3:                                      ARMS-mediated activation 0.61506276
## 4:                   AUF1 (hnRNP D0) binds and destabilizes mRNA 0.34831461
## 5:                                      AURKA Activation by TPX2 0.74462366
## 6: Abortive elongation of HIV-1 transcript in the absence of Tat 0.77405858
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.6299283 0.23112671  0.8018018  1.3863494    2    5576,112
## 2: 0.6299283 0.20895503  0.9732143  1.3147806    1        4869
## 3: 0.9149100 0.07078991  0.6875000  0.9287900    1         673
## 4: 0.8503867 0.09560315 -0.8303571 -1.1169472    1        3184
## 5: 0.9149100 0.07380527  0.4594595  0.7944250    2  22994,1454
## 6: 0.9149100 0.05960370  0.6160714  0.8322923    1        7936
## Warning:  we couldn't map to STRING 1% of your identifiers

## Note: Row-scaling applied for this heatmap

Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set3, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
## Warning in fdrtool::fdrtool(res$t, plot = FALSE, verbose = FALSE): There may be
## too few input test statistics for reliable FDR calculations!
dep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set3_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                          pathway       pval
## 1:       ADORA2B mediated anti-inflammatory cytokines production 0.03826659
## 2:                                         ALK mutants bind TKIs 0.08997955
## 3:                                      ARMS-mediated activation 0.87620890
## 4:                   AUF1 (hnRNP D0) binds and destabilizes mRNA 0.17988395
## 5:                                      AURKA Activation by TPX2 0.76515152
## 6: Abortive elongation of HIV-1 transcript in the absence of Tat 0.78143133
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.2972393 0.32177592  0.9099099  1.5191517    2    5576,112
## 2: 0.4526112 0.21392786  0.9642857  1.2982880    1        4869
## 3: 0.9688655 0.05019343 -0.5625000 -0.7611689    1         673
## 4: 0.6305824 0.14290115 -0.9107143 -1.2323686    1        3184
## 5: 0.9534660 0.04522474 -0.5045045 -0.8129068    2  1454,22994
## 6: 0.9613004 0.05547933 -0.6160714 -0.8336611    1        7936
## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

EC vs E

data_diff_EC_vs_E_pST <- test_diff(pST_se_Set3, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
## Warning in fdrtool::fdrtool(res$t, plot = FALSE, verbose = FALSE): There may be
## too few input test statistics for reliable FDR calculations!
dep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pST_Set3_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                          pathway      pval
## 1:       ADORA2B mediated anti-inflammatory cytokines production 0.3452381
## 2:                                         ALK mutants bind TKIs 0.5361446
## 3:                                      ARMS-mediated activation 0.2369478
## 4:                   AUF1 (hnRNP D0) binds and destabilizes mRNA 0.0304922
## 5:                                      AURKA Activation by TPX2 0.5008547
## 6: Abortive elongation of HIV-1 transcript in the absence of Tat 0.2188755
##         padj    log2err         ES       NES size leadingEdge
## 1: 0.9574074 0.11101149 -0.6666667 -1.105934    2    112,5576
## 2: 0.9574074 0.07569463  0.7410714  1.004114    1        4869
## 3: 0.9574074 0.12503337  0.8839286  1.197678    1         673
## 4: 0.9574074 0.35248786  0.9910714  1.342851    1        3184
## 5: 0.9574074 0.07096095  0.6529319  1.000852    2  22994,1454
## 6: 0.9574074 0.13077714  0.8928571  1.209776    1        7936
#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set3, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
## Warning in fdrtool::fdrtool(res$t, plot = FALSE, verbose = FALSE): There may be
## too few input test statistics for reliable FDR calculations!
dep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set3_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                          pathway       pval
## 1:       ADORA2B mediated anti-inflammatory cytokines production 0.34246575
## 2:                                         ALK mutants bind TKIs 0.05976096
## 3:                                      ARMS-mediated activation 0.35856574
## 4:                   AUF1 (hnRNP D0) binds and destabilizes mRNA 0.76294821
## 5:                                      AURKA Activation by TPX2 0.07758621
## 6: Abortive elongation of HIV-1 transcript in the absence of Tat 0.40039841
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.9202006 0.08479851  0.7344397  1.1154248    2    112,5576
## 2: 0.9202006 0.26166352 -0.9732143 -1.3112979    1        4869
## 3: 0.9202006 0.09754492 -0.8303571 -1.1188138    1         673
## 4: 0.9374510 0.05797548 -0.6160714 -0.8300876    1        3184
## 5: 0.9202006 0.27650060 -0.8828829 -1.4329982    2  1454,22994
## 6: 0.9202006 0.09110731 -0.8035714 -1.0827230    1        7936
#data_results <- get_df_long(dep)

Session Info

sessionInfo()
## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] lubridate_1.9.2             forcats_1.0.0              
##  [3] stringr_1.5.0               dplyr_1.1.2                
##  [5] purrr_1.0.1                 readr_2.1.4                
##  [7] tidyr_1.3.0                 tibble_3.2.1               
##  [9] ggplot2_3.4.2               tidyverse_2.0.0            
## [11] mdatools_0.14.0             SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
## [15] MatrixGenerics_1.10.0       matrixStats_1.0.0          
## [17] DEP_1.20.0                  org.Hs.eg.db_3.16.0        
## [19] AnnotationDbi_1.60.2        IRanges_2.32.0             
## [21] S4Vectors_0.36.2            Biobase_2.58.0             
## [23] BiocGenerics_0.44.0         fgsea_1.24.0               
## 
## loaded via a namespace (and not attached):
##   [1] circlize_0.4.15        fastmatch_1.1-3        plyr_1.8.8            
##   [4] igraph_1.5.0.1         gmm_1.8                lazyeval_0.2.2        
##   [7] shinydashboard_0.7.2   crosstalk_1.2.0        BiocParallel_1.32.6   
##  [10] digest_0.6.33          foreach_1.5.2          htmltools_0.5.5       
##  [13] fansi_1.0.4            magrittr_2.0.3         memoise_2.0.1         
##  [16] cluster_2.1.4          doParallel_1.0.17      tzdb_0.4.0            
##  [19] limma_3.54.2           ComplexHeatmap_2.14.0  Biostrings_2.66.0     
##  [22] imputeLCMD_2.1         sandwich_3.0-2         timechange_0.2.0      
##  [25] colorspace_2.1-0       blob_1.2.4             xfun_0.39             
##  [28] crayon_1.5.2           RCurl_1.98-1.12        jsonlite_1.8.7        
##  [31] impute_1.72.3          zoo_1.8-12             iterators_1.0.14      
##  [34] glue_1.6.2             hash_2.2.6.2           gtable_0.3.3          
##  [37] zlibbioc_1.44.0        XVector_0.38.0         GetoptLong_1.0.5      
##  [40] DelayedArray_0.24.0    shape_1.4.6            scales_1.2.1          
##  [43] pheatmap_1.0.12        vsn_3.66.0             mvtnorm_1.2-2         
##  [46] DBI_1.1.3              Rcpp_1.0.11            plotrix_3.8-2         
##  [49] mzR_2.32.0             viridisLite_0.4.2      xtable_1.8-4          
##  [52] clue_0.3-64            reactome.db_1.82.0     bit_4.0.5             
##  [55] preprocessCore_1.60.2  sqldf_0.4-11           MsCoreUtils_1.10.0    
##  [58] DT_0.28                htmlwidgets_1.6.2      httr_1.4.6            
##  [61] gplots_3.1.3           RColorBrewer_1.1-3     ellipsis_0.3.2        
##  [64] farver_2.1.1           pkgconfig_2.0.3        XML_3.99-0.14         
##  [67] sass_0.4.7             utf8_1.2.3             STRINGdb_2.10.1       
##  [70] labeling_0.4.2         tidyselect_1.2.0       rlang_1.1.1           
##  [73] later_1.3.1            munsell_0.5.0          tools_4.2.3           
##  [76] cachem_1.0.8           cli_3.6.1              gsubfn_0.7            
##  [79] generics_0.1.3         RSQLite_2.3.1          fdrtool_1.2.17        
##  [82] evaluate_0.21          fastmap_1.1.1          mzID_1.36.0           
##  [85] yaml_2.3.7             knitr_1.43             bit64_4.0.5           
##  [88] caTools_1.18.2         KEGGREST_1.38.0        ncdf4_1.21            
##  [91] mime_0.12              compiler_4.2.3         rstudioapi_0.15.0     
##  [94] plotly_4.10.2          png_0.1-8              affyio_1.68.0         
##  [97] stringi_1.7.12         bslib_0.5.0            highr_0.10            
## [100] MSnbase_2.24.2         lattice_0.21-8         ProtGenerics_1.30.0   
## [103] Matrix_1.6-0           tmvtnorm_1.5           vctrs_0.6.3           
## [106] pillar_1.9.0           norm_1.0-11.1          lifecycle_1.0.3       
## [109] BiocManager_1.30.21.1  jquerylib_0.1.4        MALDIquant_1.22.1     
## [112] GlobalOptions_0.1.2    data.table_1.14.8      cowplot_1.1.1         
## [115] bitops_1.0-7           httpuv_1.6.11          R6_2.5.1              
## [118] pcaMethods_1.90.0      affy_1.76.0            promises_1.2.0.1      
## [121] KernSmooth_2.23-22     codetools_0.2-19       MASS_7.3-60           
## [124] gtools_3.9.4           assertthat_0.2.1       chron_2.3-61          
## [127] proto_1.0.0            rjson_0.2.21           withr_2.5.0           
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3         hms_1.1.3             
## [133] grid_4.2.3             rmarkdown_2.23         shiny_1.7.4.1
knitr::knit_exit()